Detail publikace

Variational Approximation of Long-span Language Models for LVCSR

DEORAS, A. MIKOLOV, T. KOMBRINK, S. KARAFIÁT, M. KHUDANPUR, S.

Originální název

Variational Approximation of Long-span Language Models for LVCSR

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

We have presented experimental evidence that (n-gram) variational approximations of long-span LMs yield greater accuracy in LVCSR than standard n-gram models estimated from the same training text.

Klíčová slova

Recurrent Neural Network, Language Model, Variational Inference

Autoři

DEORAS, A.; MIKOLOV, T.; KOMBRINK, S.; KARAFIÁT, M.; KHUDANPUR, S.

Rok RIV

2011

Vydáno

22. 5. 2011

Nakladatel

IEEE Signal Processing Society

Místo

Praha

ISBN

978-1-4577-0537-3

Kniha

Proceedings of the 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011

Strany od

5532

Strany do

5535

Strany počet

4

URL

http://www.fit.vutbr.cz/research/groups/speech/publi/2011/deoras_icassp2011_5532.pdf

BibTex

@inproceedings{BUT76377,
  author="Anoop {Deoras} and Tomáš {Mikolov} and Stefan {Kombrink} and Martin {Karafiát} and Sanjeev {Khudanpur}",
  title="Variational Approximation of Long-span Language Models for LVCSR",
  booktitle="Proceedings of the 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011",
  year="2011",
  pages="5532--5535",
  publisher="IEEE Signal Processing Society",
  address="Praha",
  isbn="978-1-4577-0537-3",
  url="http://www.fit.vutbr.cz/research/groups/speech/publi/2011/deoras_icassp2011_5532.pdf"
}